Because the natural reservoir of Ebola virus remains unclear and diseaseoutbreaks in humans have occurred only sporadically over a large region,forecasting when and where Ebola spillovers are most likely to occur constitutesa continuing and urgent public health challenge. We developed a statisticalmodeling approach that associates 37 human or great ape Ebola spillovers since1982 with spatiotemporally dynamic covariates including vegetative cover, humanpopulation size, and absolute and relative rainfall over 3 decades acrosssub-Saharan Africa. Our model (area under the curve 0.80 on test data) showsthat spillover intensity is highest during transitions between wet and dryseasons; overall, high seasonal intensity occurs over much of tropical Africa;and spillover intensity is greatest at high (&gt;1,000/km2) and verylow (&lt;100/km2) human population densities compared withintermediate levels. These results suggest strong seasonality in Ebola spilloverfrom wild reservoirs and indicate particular times and regions for targetedsurveillance.

Mentions:
The effect of human population on Ebola spillover intensity is much smaller thanclimatic or seasonal effects. The change in average annual spillover intensity didnot change markedly for much of Africa as population increased during1975–2015 (Figure 4), whereas spilloverintensity exhibited striking shifts with climate and seasonality (Video). Nevertheless, our model does showthat spillover intensity differs by human population density. Mean annual spilloverintensity was lowest where population size per 25 km2 grid cell wasintermediate (102<x<103) and highestwhere population density was low (x<100)(Technical Appendix 2 Figure 1).Large changes in spillover intensity (±5%) during 1975–2015 appear toresult mainly from population increases. In comparing 2015 to 1975 populationdensity, shifts from intermediate-to-high population densities have generatedincreased Ebola spillover intensity, particularly in West Africa and the regionsurrounding Lake Victoria, and shifts from low-to-medium population densities havereduced spillover risk. Similarly, settlements along transportation corridors haveincreased in population to intermediate densities, leading to substantial declinesin predicted spillover intensity. However, as a result of population consolidationover large areas of central Africa, some remote districts have declined inpopulation, typically increasing predicted spillover intensity (Figure 4; Figure 5).

Mentions:
The effect of human population on Ebola spillover intensity is much smaller thanclimatic or seasonal effects. The change in average annual spillover intensity didnot change markedly for much of Africa as population increased during1975–2015 (Figure 4), whereas spilloverintensity exhibited striking shifts with climate and seasonality (Video). Nevertheless, our model does showthat spillover intensity differs by human population density. Mean annual spilloverintensity was lowest where population size per 25 km2 grid cell wasintermediate (102<x<103) and highestwhere population density was low (x<100)(Technical Appendix 2 Figure 1).Large changes in spillover intensity (±5%) during 1975–2015 appear toresult mainly from population increases. In comparing 2015 to 1975 populationdensity, shifts from intermediate-to-high population densities have generatedincreased Ebola spillover intensity, particularly in West Africa and the regionsurrounding Lake Victoria, and shifts from low-to-medium population densities havereduced spillover risk. Similarly, settlements along transportation corridors haveincreased in population to intermediate densities, leading to substantial declinesin predicted spillover intensity. However, as a result of population consolidationover large areas of central Africa, some remote districts have declined inpopulation, typically increasing predicted spillover intensity (Figure 4; Figure 5).

Because the natural reservoir of Ebola virus remains unclear and diseaseoutbreaks in humans have occurred only sporadically over a large region,forecasting when and where Ebola spillovers are most likely to occur constitutesa continuing and urgent public health challenge. We developed a statisticalmodeling approach that associates 37 human or great ape Ebola spillovers since1982 with spatiotemporally dynamic covariates including vegetative cover, humanpopulation size, and absolute and relative rainfall over 3 decades acrosssub-Saharan Africa. Our model (area under the curve 0.80 on test data) showsthat spillover intensity is highest during transitions between wet and dryseasons; overall, high seasonal intensity occurs over much of tropical Africa;and spillover intensity is greatest at high (&gt;1,000/km2) and verylow (&lt;100/km2) human population densities compared withintermediate levels. These results suggest strong seasonality in Ebola spilloverfrom wild reservoirs and indicate particular times and regions for targetedsurveillance.